Build an OpenAI RAG system with document upload, semantic search and caching

Overview This workflow implements a complete Retrieval-Augmented Generation (RAG) system for document ingestion and intelligent querying.

It allows users to upload documents, convert them into vector embeddings, and query them using natural language. The system retrieves relevant document context and generates accurate AI responses while using caching to improve performance and reduce costs.

This workflow is ideal for building AI knowledge bases, document assistants, and internal search systems.

How It Works

1. Input & Configuration Receives requests via webhook (rag-system) Supports two actions: upload → process documents query → answer questions Defines: Chunk size & overlap TopK retrieval count Database table names

Document Upload Flow

Text Extraction Extracts text from uploaded PDF documents

Text Chunking Splits text into overlapping chunks for better retrieval accuracy

Document Structuring Converts chunks into structured documents

Embedding Generation Generates vector embeddings using OpenAI

Vector Storage Stores embeddings in PGVector (Postgres)

Upload Logging Logs document metadata (user, filename, timestamp)

Response Returns success message via webhook

Query Flow

Cache Check Checks if query result exists in cache (last 1 hour)

Cache Routing If cached → return cached response
If not → proceed to retrieval

Cache Hit Flow

Format Cached Response Standardizes cached output format

Respond to User Returns cached answer with cached: true

Cache Miss Flow

Vector Retrieval Retrieves top relevant document chunks from PGVector

AI Answer Generation Uses LLM with retrieved context Generates accurate, context-based answer

Cache Storage Saves query + response in database for reuse

Response Returns generated answer with cached: false

Setup Instructions

Webhook Setup Configure endpoint (rag-system) Send payload with: action: upload / query user_id document or query

OpenAI Setup Add API credentials for: Embeddings Chat model

Postgres + PGVector Enable PGVector extension Create tables: documents query_cache upload_log

Configure Parameters Adjust: Chunk size (e.g., 1000) Overlap (e.g., 200) TopK (e.g., 5)

Optional Enhancements Add authentication layer Add multi-tenant filtering (user_id)

Use Cases

AI document search systems
Internal knowledge base assistants
Customer support knowledge retrieval
Legal or compliance document analysis
SaaS AI chat with custom data

Requirements

OpenAI API key
Postgres database with PGVector
n8n instance (cloud or self-hosted)

Key Features

Full RAG architecture (upload + query)
PDF document ingestion pipeline
Semantic search with vector embeddings
Context-aware AI responses
Query caching for performance optimization
Multi-user support via metadata filtering
Scalable and modular design

Summary

A complete RAG-based AI system that enables document ingestion, semantic search, and intelligent query answering. It combines vector databases, LLMs, and caching to deliver fast, accurate, and scalable AI-powered knowledge retrieval.